Recensement
Contents
Recensement¶
Dataviz sur les données du recensement 2019 avec recoupement sur les logements
!pip install pandas seaborn dataprep
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Requirement already satisfied: soupsieve>1.2 in /opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages (from beautifulsoup4->nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8.0,>=7.5->dataprep) (2.3.2.post1)
Requirement already satisfied: webencodings in /opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages (from bleach->nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8.0,>=7.5->dataprep) (0.5.1)
Requirement already satisfied: pycparser in /opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages (from cffi>=1.0.1->argon2-cffi-bindings->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8.0,>=7.5->dataprep) (2.21)
WARNING: There was an error checking the latest version of pip.
!wget "https://data.gouv.nc/explore/dataset/rp-2019-indv-psud/download/?format=csv&timezone=Pacific/Noumea&lang=fr&use_labels_for_header=true&csv_separator=%2C" -O data/recensement_individus.csv
--2022-05-24 05:23:43-- https://data.gouv.nc/explore/dataset/rp-2019-indv-psud/download/?format=csv&timezone=Pacific/Noumea&lang=fr&use_labels_for_header=true&csv_separator=%2C
Resolving data.gouv.nc (data.gouv.nc)...
13.211.119.48, 13.55.171.246
Connecting to data.gouv.nc (data.gouv.nc)|13.211.119.48|:443...
connected.
HTTP request sent, awaiting response...
200 OK
Length: unspecified [application/csv]
Saving to: ‘data/recensement_individus.csv’
data/rece [<=> ] 0 --.-KB/s
data/recen [ <=> ] 36.68K 182KB/s
data/recens [ <=> ] 100.67K 250KB/s
data/recense [ <=> ] 212.64K 352KB/s
data/recensem [ <=> ] 308.63K 311KB/s
data/recenseme [ <=> ] 388.61K 295KB/s
data/recensemen [ <=> ] 500.59K 297KB/s
data/recensement [ <=> ] 596.57K 296KB/s
data/recensement_ [ <=> ] 676.55K 287KB/s
data/recensement_i [ <=> ] 764.55K 285KB/s
data/recensement_in [ <=> ] 860.55K 286KB/s
ata/recensement_ind [ <=> ] 924.55K 278KB/s
ta/recensement_indi [ <=> ] 1005K 274KB/s
a/recensement_indiv [ <=> ] 1.09M 280KB/s
/recensement_indivi [ <=> ] 1.18M 281KB/s
recensement_individ [ <=> ] 1.25M 275KB/s
ecensement_individu [ <=> ] 1.32M 265KB/s
censement_individus [ <=>] 1.43M 263KB/s
ensement_individus. [ <=> ] 1.45M 254KB/s
nsement_individus.c [ <=> ] 1.50M 246KB/s
sement_individus.cs [ <=> ] 1.56M 250KB/s
ement_individus.csv [ <=> ] 1.57M 236KB/s
ment_individus.csv [ <=> ] 1.65M 228KB/s
ent_individus.csv [ <=> ] 1.68M 221KB/s
nt_individus.csv [ <=> ] 1.71M 217KB/s
t_individus.csv [ <=> ] 1.78M 211KB/s
_individus.csv [ <=> ] 1.84M 191KB/s
individus.csv [ <=> ] 1.90M 191KB/s
ndividus.csv [ <=> ] 1.93M 175KB/s
dividus.csv [ <=> ] 1.96M 167KB/s
ividus.csv [ <=> ] 2.03M 165KB/s
vidus.csv [ <=> ] 2.09M 148KB/s
idus.csv [ <=> ] 2.15M 145KB/s
dus.csv [ <=> ] 2.18M 141KB/s
us.csv [<=> ] 2.21M 138KB/s
s.csv [ <=> ] 2.28M 132KB/s
.csv [ <=> ] 2.34M 133KB/s
csv [ <=> ] 2.40M 132KB/s
sv [ <=> ] 2.43M 136KB/s
v [ <=> ] 2.50M 134KB/s
[ <=> ] 2.53M 133KB/s
d [ <=> ] 2.56M 134KB/s
da [ <=> ] 2.62M 133KB/s
dat [ <=> ] 2.65M 129KB/s
data [ <=> ] 2.68M 130KB/s
data/ [ <=> ] 2.72M 128KB/s
data/r [ <=> ] 2.78M 134KB/s
data/re [ <=> ] 2.81M 135KB/s
data/rec [ <=> ] 2.87M 135KB/s
data/rece [ <=> ] 2.90M 133KB/s
data/recen [ <=> ] 2.93M 135KB/s
data/recens [ <=>] 3.00M 134KB/s
data/recense [ <=> ] 3.03M 130KB/s
data/recensem [ <=> ] 3.08M 135KB/s
data/recenseme [ <=> ] 3.12M 132KB/s
data/recensemen [ <=> ] 3.15M 130KB/s
data/recensement [ <=> ] 3.18M 132KB/s
data/recensement_ [ <=> ] 3.25M 132KB/s
data/recensement_i [ <=> ] 3.28M 130KB/s
data/recensement_in [ <=> ] 3.31M 124KB/s
ata/recensement_ind [ <=> ] 3.37M 129KB/s
ta/recensement_indi [ <=> ] 3.40M 128KB/s
a/recensement_indiv [ <=> ] 3.43M 127KB/s
/recensement_indivi [ <=> ] 3.50M 133KB/s
recensement_individ [ <=> ] 3.53M 135KB/s
ecensement_individu [ <=> ] 3.56M 132KB/s
censement_individus [ <=> ] 3.62M 136KB/s
ensement_individus. [ <=> ] 3.65M 135KB/s
nsement_individus.c [<=> ] 3.68M 130KB/s
sement_individus.cs [ <=> ] 3.78M 134KB/s
ement_individus.csv [ <=> ] 3.81M 135KB/s
ment_individus.csv [ <=> ] 3.87M 137KB/s
ent_individus.csv [ <=> ] 3.90M 142KB/s
nt_individus.csv [ <=> ] 3.93M 136KB/s
t_individus.csv [ <=> ] 4.00M 139KB/s
_individus.csv [ <=> ] 4.03M 134KB/s
individus.csv [ <=> ] 4.06M 134KB/s
ndividus.csv [ <=> ] 4.12M 132KB/s
dividus.csv [ <=> ] 4.15M 135KB/s
ividus.csv [ <=> ] 4.18M 134KB/s
vidus.csv [ <=> ] 4.25M 136KB/s
idus.csv [ <=> ] 4.28M 134KB/s
dus.csv [ <=> ] 4.31M 134KB/s
us.csv [ <=> ] 4.37M 131KB/s
s.csv [ <=> ] 4.40M 134KB/s
.csv [ <=>] 4.43M 133KB/s
csv [ <=> ] 4.50M 134KB/s
sv [ <=> ] 4.53M 135KB/s
v [ <=> ] 4.59M 142KB/s
[ <=> ] 4.62M 136KB/s
d [ <=> ] 4.65M 135KB/s
da [ <=> ] 4.71M 131KB/s
dat [ <=> ] 4.75M 131KB/s
data [ <=> ] 4.78M 132KB/s
data/ [ <=> ] 4.84M 131KB/s
data/r [ <=> ] 4.87M 134KB/s
data/re [ <=> ] 4.90M 135KB/s
data/rec [ <=> ] 4.96M 137KB/s
data/rece [ <=> ] 5.00M 137KB/s
data/recen [ <=> ] 5.03M 136KB/s
data/recens [ <=> ] 5.09M 138KB/s
data/recense [ <=> ] 5.12M 138KB/s
data/recensem [<=> ] 5.15M 138KB/s
data/recenseme [ <=> ] 5.21M 139KB/s
data/recensemen [ <=> ] 5.25M 138KB/s
data/recensement [ <=> ] 5.28M 137KB/s
data/recensement_ [ <=> ] 5.34M 137KB/s
data/recensement_i [ <=> ] 5.37M 133KB/s
data/recensement_in [ <=> ] 5.42M 133KB/s
ata/recensement_ind [ <=> ] 5.46M 132KB/s
ta/recensement_indi [ <=> ] 5.50M 133KB/s
a/recensement_indiv [ <=> ] 5.53M 131KB/s
/recensement_indivi [ <=> ] 5.59M 135KB/s
recensement_individ [ <=> ] 5.62M 136KB/s
ecensement_individu [ <=> ] 5.65M 132KB/s
censement_individus [ <=> ] 5.71M 135KB/s
ensement_individus. [ <=> ] 5.75M 135KB/s
nsement_individus.c [ <=> ] 5.78M 126KB/s
sement_individus.cs [ <=> ] 5.82M 128KB/s
ement_individus.csv [ <=>] 5.87M 128KB/s
ment_individus.csv [ <=> ] 5.93M 128KB/s
ent_individus.csv [ <=> ] 5.96M 128KB/s
nt_individus.csv [ <=> ] 6.00M 128KB/s
t_individus.csv [ <=> ] 6.06M 124KB/s
_individus.csv [ <=> ] 6.09M 125KB/s
individus.csv [ <=> ] 6.12M 125KB/s
ndividus.csv [ <=> ] 6.18M 124KB/s
dividus.csv [ <=> ] 6.21M 126KB/s
ividus.csv [ <=> ] 6.25M 124KB/s
vidus.csv [ <=> ] 6.31M 126KB/s
idus.csv [ <=> ] 6.34M 126KB/s
dus.csv [ <=> ] 6.37M 127KB/s
us.csv [ <=> ] 6.43M 127KB/s
s.csv [ <=> ] 6.46M 129KB/s
.csv [ <=> ] 6.50M 127KB/s
csv [ <=> ] 6.56M 130KB/s
sv [<=> ] 6.59M 129KB/s
v [ <=> ] 6.65M 137KB/s
[ <=> ] 6.68M 134KB/s
d [ <=> ] 6.71M 132KB/s
da [ <=> ] 6.78M 133KB/s
dat [ <=> ] 6.81M 133KB/s
data [ <=> ] 6.84M 133KB/s
data/ [ <=> ] 6.90M 140KB/s
data/r [ <=> ] 6.93M 138KB/s
data/re [ <=> ] 6.96M 139KB/s
data/rec [ <=> ] 7.03M 141KB/s
data/rece [ <=> ] 7.06M 142KB/s
data/recen [ <=> ] 7.09M 142KB/s
data/recens [ <=> ] 7.15M 142KB/s
data/recense [ <=> ] 7.21M 143KB/s
data/recensem [ <=> ] 7.28M 143KB/s
data/recenseme [ <=> ] 7.31M 143KB/s
data/recensemen [ <=>] 7.37M 148KB/s
data/recensement [ <=> ] 7.40M 143KB/s
data/recensement_ [ <=> ] 7.43M 142KB/s
data/recensement_i [ <=> ] 7.50M 140KB/s
data/recensement_in [ <=> ] 7.53M 140KB/s
ata/recensement_ind [ <=> ] 7.56M 144KB/s
ta/recensement_indi [ <=> ] 7.62M 144KB/s
a/recensement_indiv [ <=> ] 7.65M 144KB/s
/recensement_indivi [ <=> ] 7.68M 144KB/s
recensement_individ [ <=> ] 7.75M 141KB/s
ecensement_individu [ <=> ] 7.78M 142KB/s
censement_individus [ <=> ] 7.81M 132KB/s
ensement_individus. [ <=> ] 7.84M 129KB/s
nsement_individus.c [ <=> ] 7.90M 125KB/s
sement_individus.cs [ <=> ] 7.93M 124KB/s
ement_individus.csv [ <=> ] 8.00M 121KB/s
ment_individus.csv [ <=> ] 8.03M 116KB/s
ent_individus.csv [<=> ] 8.06M 113KB/s
nt_individus.csv [ <=> ] 8.09M 109KB/s
t_individus.csv [ <=> ] 8.15M 109KB/s
_individus.csv [ <=> ] 8.18M 103KB/s
individus.csv [ <=> ] 8.25M 106KB/s
ndividus.csv [ <=> ] 8.28M 104KB/s
dividus.csv [ <=> ] 8.34M 103KB/s
ividus.csv [ <=> ] 8.37M 103KB/s
vidus.csv [ <=> ] 8.40M 104KB/s
idus.csv [ <=> ] 8.46M 103KB/s
dus.csv [ <=> ] 8.50M 102KB/s
us.csv [ <=> ] 8.53M 102KB/s
s.csv [ <=> ] 8.59M 103KB/s
.csv [ <=> ] 8.62M 103KB/s
csv [ <=> ] 8.65M 109KB/s
sv [ <=> ] 8.71M 110KB/s
v [ <=> ] 8.75M 111KB/s
[ <=>] 8.78M 113KB/s
d [ <=> ] 8.84M 116KB/s
da [ <=> ] 8.87M 120KB/s
dat [ <=> ] 8.93M 128KB/s
data [ <=> ] 8.96M 128KB/s
data/ [ <=> ] 9.00M 128KB/s
data/r [ <=> ] 9.06M 135KB/s
data/re [ <=> ] 9.09M 133KB/s
data/rec [ <=> ] 9.12M 135KB/s
data/rece [ <=> ] 9.18M 139KB/s
data/recen [ <=> ] 9.21M 141KB/s
data/recens [ <=> ] 9.25M 140KB/s
data/recense [ <=> ] 9.31M 140KB/s
data/recensem [ <=> ] 9.34M 141KB/s
data/recenseme [ <=> ] 9.37M 140KB/s
data/recensemen [ <=> ] 9.43M 136KB/s
data/recensement [ <=> ] 9.46M 137KB/s
data/recensement_ [<=> ] 9.50M 135KB/s
data/recensement_i [ <=> ] 9.53M 135KB/s
data/recensement_in [ <=> ] 9.59M 135KB/s
ata/recensement_ind [ <=> ] 9.65M 140KB/s
ta/recensement_indi [ <=> ] 9.68M 134KB/s
a/recensement_indiv [ <=> ] 9.71M 135KB/s
/recensement_indivi [ <=> ] 9.78M 135KB/s
recensement_individ [ <=> ] 9.81M 133KB/s
ecensement_individu [ <=> ] 9.84M 134KB/s
censement_individus [ <=> ] 9.90M 134KB/s
ensement_individus. [ <=> ] 9.93M 133KB/s
nsement_individus.c [ <=> ] 9.96M 134KB/s
sement_individus.cs [ <=> ] 10.03M 134KB/s
ement_individus.csv [ <=> ] 10.06M 133KB/s
ment_individus.csv [ <=> ] 10.09M 131KB/s
ent_individus.csv [ <=> ] 10.15M 131KB/s
nt_individus.csv [ <=> ] 10.18M 129KB/s
t_individus.csv [ <=>] 10.21M 130KB/s
_individus.csv [ <=> ] 10.28M 132KB/s
individus.csv [ <=> ] 10.31M 130KB/s
ndividus.csv [ <=> ] 10.34M 133KB/s
dividus.csv [ <=> ] 10.40M 136KB/s
ividus.csv [ <=> ] 10.43M 134KB/s
vidus.csv [ <=> ] 10.46M 129KB/s
idus.csv [ <=> ] 10.53M 133KB/s
dus.csv [ <=> ] 10.56M 133KB/s
us.csv [ <=> ] 10.59M 127KB/s
s.csv [ <=> ] 10.65M 133KB/s
.csv [ <=> ] 10.68M 133KB/s
csv [ <=> ] 10.71M 128KB/s
sv [ <=> ] 10.78M 131KB/s
v [ <=> ] 10.81M 130KB/s
[ <=> ] 10.87M 130KB/s
d [ <=> ] 10.90M 130KB/s
da [<=> ] 10.93M 132KB/s
dat [ <=> ] 11.00M 132KB/s
data [ <=> ] 11.03M 133KB/s
data/ [ <=> ] 11.06M 133KB/s
data/r [ <=> ] 11.12M 136KB/s
data/re [ <=> ] 11.15M 137KB/s
data/rec [ <=> ] 11.18M 137KB/s
data/rece [ <=> ] 11.25M 137KB/s
data/recen [ <=> ] 11.28M 136KB/s
data/recens [ <=> ] 11.31M 136KB/s
data/recense [ <=> ] 11.37M 137KB/s
data/recensem [ <=> ] 11.40M 137KB/s
data/recenseme [ <=> ] 11.43M 131KB/s
data/recensemen [ <=> ] 11.50M 131KB/s
data/recensement [ <=> ] 11.53M 129KB/s
data/recensement_ [ <=> ] 11.56M 131KB/s
data/recensement_i [ <=> ] 11.62M 130KB/s
data/recensement_in [ <=>] 11.65M 131KB/s
ata/recensement_ind [ <=> ] 11.68M 126KB/s
ta/recensement_indi [ <=> ] 11.75M 131KB/s
a/recensement_indiv [ <=> ] 11.78M 131KB/s
/recensement_indivi [ <=> ] 11.81M 126KB/s
recensement_individ [ <=> ] 11.87M 129KB/s
ecensement_individu [ <=> ] 11.90M 130KB/s
censement_individus [ <=> ] 11.96M 128KB/s
ensement_individus. [ <=> ] 12.00M 129KB/s
nsement_individus.c [ <=> ] 12.03M 129KB/s
sement_individus.cs [ <=> ] 12.09M 129KB/s
ement_individus.csv [ <=> ] 12.12M 129KB/s
ment_individus.csv [ <=> ] 12.15M 129KB/s
ent_individus.csv [ <=> ] 12.21M 127KB/s
nt_individus.csv [ <=> ] 12.25M 127KB/s
t_individus.csv [ <=> ] 12.28M 131KB/s
_individus.csv [ <=> ] 12.31M 128KB/s
individus.csv [<=> ] 12.37M 133KB/s
ndividus.csv [ <=> ] 12.40M 131KB/s
dividus.csv [ <=> ] 12.46M 134KB/s
ividus.csv [ <=> ] 12.50M 132KB/s
vidus.csv [ <=> ] 12.56M 137KB/s
idus.csv [ <=> ] 12.59M 133KB/s
dus.csv [ <=> ] 12.62M 133KB/s
us.csv [ <=> ] 12.68M 136KB/s
s.csv [ <=> ] 12.71M 133KB/s
.csv [ <=> ] 12.75M 132KB/s
csv [ <=> ] 12.81M 133KB/s
sv [ <=> ] 12.84M 134KB/s
v [ <=> ] 12.87M 129KB/s
[ <=> ] 12.93M 128KB/s
d [ <=> ] 12.96M 128KB/s
da [ <=> ] 13.00M 128KB/s
dat [ <=> ] 13.06M 131KB/s
data [ <=>] 13.09M 131KB/s
data/ [ <=> ] 13.12M 133KB/s
data/r [ <=> ] 13.18M 135KB/s
data/re [ <=> ] 13.21M 133KB/s
data/rec [ <=> ] 13.25M 134KB/s
data/rece [ <=> ] 13.31M 134KB/s
data/recen [ <=> ] 13.34M 134KB/s
data/recens [ <=> ] 13.37M 130KB/s
data/recense [ <=> ] 13.43M 134KB/s
data/recensem [ <=> ] 13.46M 132KB/s
data/recenseme [ <=> ] 13.53M 133KB/s
data/recensemen [ <=> ] 13.56M 132KB/s
data/recensement [ <=> ] 13.59M 132KB/s
data/recensement_ [ <=> ] 13.65M 133KB/s
data/recensement_i [ <=> ] 13.68M 131KB/s
data/recensement_in [ <=> ] 13.71M 135KB/s
ata/recensement_ind [ <=> ] 13.78M 137KB/s
ta/recensement_indi [<=> ] 13.81M 137KB/s
a/recensement_indiv [ <=> ] 13.84M 137KB/s
/recensement_indivi [ <=> ] 13.90M 138KB/s
recensement_individ [ <=> ] 13.93M 139KB/s
ecensement_individu [ <=> ] 13.96M 137KB/s
censement_individus [ <=> ] 14.03M 138KB/s
ensement_individus. [ <=> ] 14.06M 137KB/s
nsement_individus.c [ <=> ] 14.09M 136KB/s
sement_individus.cs [ <=> ] 14.15M 137KB/s
ement_individus.csv [ <=> ] 14.18M 134KB/s
ment_individus.csv [ <=> ] 14.25M 140KB/s
ent_individus.csv [ <=> ] 14.28M 135KB/s
nt_individus.csv [ <=> ] 14.31M 131KB/s
t_individus.csv [ <=> ] 14.37M 133KB/s
_individus.csv [ <=> ] 14.40M 133KB/s
individus.csv [ <=> ] 14.43M 132KB/s
ndividus.csv [ <=> ] 14.50M 123KB/s
dividus.csv [ <=>] 14.53M 123KB/s
ividus.csv [ <=> ] 14.56M 123KB/s
vidus.csv [ <=> ] 14.62M 122KB/s
idus.csv [ <=> ] 14.65M 123KB/s
dus.csv [ <=> ] 14.68M 123KB/s
us.csv [ <=> ] 14.75M 124KB/s
s.csv [ <=> ] 14.78M 123KB/s
.csv [ <=> ] 14.84M 129KB/s
csv [ <=> ] 14.87M 124KB/s
sv [ <=> ] 14.90M 122KB/s
v [ <=> ] 14.96M 126KB/s
[ <=> ] 15.00M 121KB/s
d [ <=> ] 15.03M 123KB/s
da [ <=> ] 15.09M 122KB/s
dat [ <=> ] 15.12M 122KB/s
data [ <=> ] 15.15M 127KB/s
data/ [ <=> ] 15.21M 126KB/s
data/r [<=> ] 15.25M 126KB/s
data/re [ <=> ] 15.34M 132KB/s
data/rec [ <=> ] 15.37M 137KB/s
data/rece [ <=> ] 15.40M 137KB/s
data/recen [ <=> ] 15.46M 146KB/s
data/recens [ <=> ] 15.50M 137KB/s
data/recense [ <=> ] 15.56M 143KB/s
data/recensem [ <=> ] 15.59M 141KB/s
data/recenseme [ <=> ] 15.62M 135KB/s
data/recensemen [ <=> ] 15.68M 136KB/s
data/recensement [ <=> ] 15.71M 134KB/s
data/recensement_ [ <=> ] 15.75M 132KB/s
data/recensement_i [ <=> ] 15.81M 141KB/s
data/recensement_in [ <=> ] 15.84M 136KB/s
ata/recensement_ind [ <=> ] 15.87M 137KB/s
ta/recensement_indi [ <=> ] 15.93M 141KB/s
a/recensement_indiv [ <=> ] 15.96M 138KB/s
/recensement_indivi [ <=>] 16.00M 137KB/s
recensement_individ [ <=> ] 16.06M 143KB/s
ecensement_individu [ <=> ] 16.09M 134KB/s
censement_individus [ <=> ] 16.15M 141KB/s
ensement_individus. [ <=> ] 16.18M 135KB/s
nsement_individus.c [ <=> ] 16.21M 135KB/s
sement_individus.cs [ <=> ] 16.28M 140KB/s
ement_individus.csv [ <=> ] 16.31M 133KB/s
ment_individus.csv [ <=> ] 16.34M 134KB/s
ent_individus.csv [ <=> ] 16.40M 132KB/s
nt_individus.csv [ <=> ] 16.43M 132KB/s
t_individus.csv [ <=> ] 16.46M 133KB/s
_individus.csv [ <=> ] 16.53M 138KB/s
individus.csv [ <=> ] 16.56M 136KB/s
ndividus.csv [ <=> ] 16.59M 136KB/s
dividus.csv [ <=> ] 16.65M 137KB/s
ividus.csv [ <=> ] 16.68M 135KB/s
vidus.csv [<=> ] 16.71M 135KB/s
idus.csv [ <=> ] 16.78M 135KB/s
dus.csv [ <=> ] 16.81M 134KB/s
us.csv [ <=> ] 16.84M 131KB/s
s.csv [ <=> ] 16.90M 130KB/s
.csv [ <=> ] 16.93M 128KB/s
csv [ <=> ] 17.00M 126KB/s
sv [ <=> ] 17.03M 122KB/s
v [ <=> ] 17.06M 123KB/s
[ <=> ] 17.12M 123KB/s
data/recensement_in [ <=> ] 17.14M 125KB/s in 2m 8s
2022-05-24 05:25:53 (137 KB/s) - ‘data/recensement_individus.csv’ saved [17971968]
!wget "https://data.gouv.nc/explore/dataset/rp-2019-logements/download/?format=csv&timezone=Pacific/Noumea&lang=fr&use_labels_for_header=true&csv_separator=%2C" -O data/recensement_logements.csv
--2022-05-24 05:25:53-- https://data.gouv.nc/explore/dataset/rp-2019-logements/download/?format=csv&timezone=Pacific/Noumea&lang=fr&use_labels_for_header=true&csv_separator=%2C
Resolving data.gouv.nc (data.gouv.nc)... 13.211.119.48, 13.55.171.246
Connecting to data.gouv.nc (data.gouv.nc)|13.211.119.48|:443...
connected.
HTTP request sent, awaiting response...
200 OK
Length: unspecified [application/csv]
Saving to: ‘data/recensement_logements.csv’
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2022-05-24 05:26:57 (178 KB/s) - ‘data/recensement_logements.csv’ saved [11293731]
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
df = pd.read_csv("data/recensement_individus.csv")
df['COUPLE'] = df['COUPLE'].astype("category")
df['COUPLE'] = df['COUPLE'].cat.rename_categories({1: 'Vit en couple', 2: 'ne vit pas en couple'})
df['CS24'] = df['CS24'].astype("category")
df['CS24'] = df['CS24'].cat.rename_categories({10: 'Agriculteurs exploitants', 21: 'Artisans', 22: 'Commerçants et assimilés', 23: 'Chefs d\'entreprise de 10 salariés ou plus',
31: 'Professions libérales et assimilés', 32: 'Cadres de la fonction publique, professions intellectuelles et artistiques',
36: 'Cadres d\'entreprise', 41: 'Professions intermédiaires de l\'enseignement, de la santé, de la fonction publique et assimilés',
46: 'Professions intermédiaires administratives et commerciales des entreprises', 47: 'Techniciens',
48: 'Contremaîtres, agents de maîtrise', 51: 'Employés de la fonction publique',
54: 'Employés administratifs d\'entreprise', 55: 'Employés de commerce', 56: 'Personnels des services directs aux particuliers',
61: 'Ouvriers qualifiés', 66: 'Ouvriers non qualifiés', 69: 'Ouvriers agricoles'})
df['CS42'] = df['CS42'].astype("category")
df['CS42'] = df['CS42'].cat.rename_categories({11: 'Agriculteurs sur petites exploitations', 12: 'Agriculteurs sur moyennes exploitations', 13: 'Agriculteurs sur grandes exploitations',
21: 'Artisans', 22: 'Commerçants et assimilés' ,23: 'Chefs d\'entreprise de 10 salariés ou plus',
31: 'Professions libérales et assimilés', 33: 'Cadres de la fonction publique', 34: 'Professeurs, professions scientifiques',
35: 'Professions de l\'information, des arts et des spectacles', 37: 'Cadres administratifs et commerciaux d\'entreprise',
38: 'Ingénieurs et cadres techniques d\'entreprise', 42: 'Professeurs des écoles, instituteurs et assimilés',
43: 'Professions intermédiaires de la santé et du travail social',
44: 'Clergé, religieux', 45: 'Professions intermédiaires administratives de la fonction publique',
46: 'Professions intermédiaires administratives et commerciales des entreprises', 47: 'Techniciens',
48: 'Contremaîtres, agents de maîtrise', 52: 'Employés civils et agents de service de la fonction publique',
53: 'Policiers et militaires', 54: 'Employés administratifs d\'entreprise', 55: 'Employés de commerce',
56: 'Personnels des services directs aux particuliers', 62: 'Ouvriers qualifiés de type industriel',
63: 'Ouvriers qualifiés de type artisanal', 64: 'Chauffeurs', 65: 'Ouvriers qualifiés de la manutention, du magasinage et du transport',
67: 'Ouvriers non qualifiés de type industriel', 68: 'Ouvriers non qualifiés de type artisanal',
69: 'Ouvriers agricoles'})
df['CS8'] = df['CS8'].astype("category")
df['CS8'] = df['CS8'].cat.rename_categories({1: 'Agriculteurs exploitants', 2: 'Artisans, commerçants et chefs d\'entreprise',
3: 'Cadres et professions intellectuelles supérieures', 4 : 'Professions Intermédiaires',
5: 'Employés', 6: 'Ouvriers'})
df['CSSAL'] = df['CSSAL'].astype("category")
df['CSSAL'] = df['CSSAL'].cat.rename_categories({1: 'Manœuvre, ouvrier spécialisé', 2: 'Ouvrier qualifié ou hautement qualifié, technicien d’atelier',
3: 'Technicien (non cadre)', 4 : 'Agent de catégorie B de la fonction publique',
5: 'Agent de maîtrise, maîtrise administrative ou commerciale, VRP', 6: 'Agent de catégorie A de la fonction publique',
7: 'Ingénieur, cadre d’entreprise', 8: 'Agent de catégorie C ou D de la fonction publique',
9: 'Employé (par exemple : de bureau, de commerce, de la restauration, de maison)'})
EMPL_labels = {3: 'Artisan, commerçant, industriel, travailleur indépendant', 4: 'Stagiaire rémunéré, apprenti sous contrat',
5: 'Salarié du secteur privé à durée déterminée', 6: 'Salarié du secteur privé à durée indéterminée',
7 : 'Salarié du secteur public à durée déterminée', 8: 'Salarié du secteur public à durée indéterminé', }
df['EMPL'] = df['EMPL'].astype("category")
df['DIPL'] = df['DIPL'].astype("category")
diplomes_libelles = {1: 'Pas de scolarisation', 2: 'Aucun diplôme mais scolarisation jusqu’en primaire', 3: 'Aucun diplôme mais scolarisation jusqu’au collège',
4: 'Aucun diplôme mais scolarisation au-delà du collège', 11: 'CEP' , 12: 'BEPC, brevet élémentaire, brevet des collèges, DNB' , 13: 'CAP, BEP ou diplôme de niveau équivalent',
14: 'Bac général ou technologique, brevet supérieur, capacité en droit, DAEU, ESEU',
15: 'Bac professionnel, brevet professionnel de technicien ou d’enseignement, diplôme équivalent',
16: 'BTS, DUT, Deug, Deust, diplôme de santé ou du social niveau bac + 2, diplôme équivalent',
17: 'Licence, Licence pro, maîtrise, diplôme équivalent de niveau bac + 3 ou bac + 4',
18: 'Master, DEA, diplôme grande école niveau bac + 5, doctorat de santé',
19: 'Doctorat de recherche (hors santé)'}
#df['DIPL'] = df['DIPL'].cat.rename_categories(diplomes_libelles)
#df['CS8'] = df['CS8'].astype("category")
#df['CS8'] = df['CS8'].cat.rename_categories({ : '',: '', : '',: '', : '', })
#df['CS8'] = df['CS8'].astype("category")
#df['CS8'] = df['CS8'].cat.rename_categories({ : '',: '', : '',: '', : '', })
df.head()
| ID | IDLOG | AGEA | AGER | ANNINS | APE | CNAT | COUPLE | CPAYSN | CPAYSRA | ... | STAT | STATANT | STM | TACT | TP | TRAANT | TRANS | TYP | TYPEMPL | TYPMENR | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 200296 | 85580.0 | 7 | 7 | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | 6 | NaN | NaN | NaN | NaN | 2 | NaN | 3.0 |
| 1 | 200301 | 48282.0 | 46 | 46 | NaN | 2410Z | NaN | Vit en couple | NaN | NaN | ... | 3.0 | NaN | 2 | 1.0 | 1.0 | NaN | 4.0 | 2 | 1.0 | 3.0 |
| 2 | 200307 | 477.0 | 15 | 15 | NaN | NaN | NaN | ne vit pas en couple | NaN | NaN | ... | NaN | NaN | 6 | 3.0 | NaN | 2.0 | 5.0 | 2 | NaN | 3.0 |
| 3 | 200315 | 94323.0 | 40 | 40 | NaN | 8411Z | NaN | Vit en couple | NaN | NaN | ... | 3.0 | NaN | 3 | 1.0 | 1.0 | NaN | 4.0 | 2 | 1.0 | 3.0 |
| 4 | 200318 | 107640.0 | 68 | 68 | 1972.0 | NaN | NaN | Vit en couple | NaN | NaN | ... | NaN | 1.0 | 1 | 5.0 | NaN | 1.0 | 4.0 | 2 | NaN | 5.0 |
5 rows × 43 columns
df.describe()
| ID | IDLOG | AGEA | AGER | ANNINS | CNAT | CPAYSN | CPAYSRA | EXER | GAD | ... | STAT | STATANT | STM | TACT | TP | TRAANT | TRANS | TYP | TYPEMPL | TYPMENR | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 203144.000000 | 199929.000000 | 203144.000000 | 203144.000000 | 44082.000000 | 2412.000000 | 3137.00000 | 131.000000 | 89638.000000 | 203144.000000 | ... | 89155.000000 | 41032.000000 | 203144.000000 | 162368.000000 | 89638.000000 | 61332.000000 | 162852.000000 | 203144.000000 | 68942.000000 | 199844.000000 |
| mean | 135704.624434 | 54598.597797 | 35.574174 | 35.283617 | 1999.589651 | 432.129353 | 476.13803 | 454.458015 | 1.081428 | 3.085555 | ... | 2.755224 | 1.116494 | 3.879258 | 2.618632 | 1.114003 | 1.281729 | 3.701183 | 2.016245 | 1.252894 | 3.270726 |
| std | 78350.449683 | 31455.117174 | 21.826895 | 21.821489 | 17.898269 | 268.332958 | 76.04784 | 124.236639 | 0.328495 | 2.186296 | ... | 0.810840 | 0.379688 | 2.129446 | 1.953191 | 0.317817 | 0.449846 | 1.125026 | 0.126415 | 0.717101 | 1.215611 |
| min | 1.000000 | 2.000000 | 0.000000 | 0.000000 | 1927.000000 | 103.000000 | 127.00000 | 132.000000 | 1.000000 | 0.000000 | ... | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 2.000000 | 1.000000 | 1.000000 |
| 25% | 67790.500000 | 27344.000000 | 17.000000 | 17.000000 | 1988.000000 | 219.000000 | 501.00000 | 501.000000 | 1.000000 | 1.000000 | ... | 3.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 4.000000 | 2.000000 | 1.000000 | 3.000000 |
| 50% | 135765.000000 | 54590.000000 | 35.000000 | 34.000000 | 2006.000000 | 416.000000 | 514.00000 | 501.000000 | 1.000000 | 3.000000 | ... | 3.000000 | 1.000000 | 4.000000 | 1.000000 | 1.000000 | 1.000000 | 4.000000 | 2.000000 | 1.000000 | 3.000000 |
| 75% | 203519.250000 | 81991.000000 | 52.000000 | 51.000000 | 2015.000000 | 514.000000 | 514.00000 | 501.000000 | 1.000000 | 5.000000 | ... | 3.000000 | 1.000000 | 6.000000 | 5.000000 | 1.000000 | 2.000000 | 4.000000 | 2.000000 | 1.000000 | 4.000000 |
| max | 271406.000000 | 109025.000000 | 104.000000 | 104.000000 | 2019.000000 | 999.000000 | 514.00000 | 514.000000 | 3.000000 | 9.000000 | ... | 9.000000 | 3.000000 | 6.000000 | 7.000000 | 2.000000 | 2.000000 | 5.000000 | 3.000000 | 9.000000 | 5.000000 |
8 rows × 30 columns
from dataprep.eda import plot
plot(df)
Dataset Statistics
| Number of Variables | 43 |
|---|---|
| Number of Rows | 203144 |
| Missing Cells | 3.5524e+06 |
| Missing Cells (%) | 40.7% |
| Duplicate Rows | 0 |
| Duplicate Rows (%) | 0.0% |
| Total Size in Memory | 103.8 MB |
| Average Row Size in Memory | 535.9 B |
| Variable Types |
|
Dataset Insights
| ID is uniformly distributed | Uniform |
|---|---|
| AGEA and AGER have similar distributions | Similar Distribution |
| CNAT and CPAYSN have similar distributions | Similar Distribution |
| IDLOG has 3215 (1.58%) missing values | Missing |
| ANNINS has 159062 (78.3%) missing values | Missing |
| APE has 156869 (77.22%) missing values | Missing |
| CNAT has 200732 (98.81%) missing values | Missing |
| COUPLE has 40776 (20.07%) missing values | Missing |
| CPAYSN has 200007 (98.46%) missing values | Missing |
| CPAYSRA has 203013 (99.94%) missing values | Missing |
Dataset Insights
| CS24 has 128604 (63.31%) missing values | Missing |
|---|---|
| CS42 has 128604 (63.31%) missing values | Missing |
| CS8 has 128604 (63.31%) missing values | Missing |
| CSSAL has 140423 (69.12%) missing values | Missing |
| DIPL has 40776 (20.07%) missing values | Missing |
| EMPL has 113506 (55.87%) missing values | Missing |
| EXER has 113506 (55.87%) missing values | Missing |
| IRA has 13748 (6.77%) missing values | Missing |
| MINE has 199871 (98.39%) missing values | Missing |
| PROVRA has 13748 (6.77%) missing values | Missing |
Dataset Insights
| PROVTRA has 113506 (55.87%) missing values | Missing |
|---|---|
| RECH has 144665 (71.21%) missing values | Missing |
| SAL has 191662 (94.35%) missing values | Missing |
| SCOL has 26951 (13.27%) missing values | Missing |
| SECT10 has 113506 (55.87%) missing values | Missing |
| SECT21 has 113506 (55.87%) missing values | Missing |
| SECT5 has 113506 (55.87%) missing values | Missing |
| STAT has 113989 (56.11%) missing values | Missing |
| STATANT has 162112 (79.8%) missing values | Missing |
| TACT has 40776 (20.07%) missing values | Missing |
Dataset Insights
| TP has 113506 (55.87%) missing values | Missing |
|---|---|
| TRAANT has 141812 (69.81%) missing values | Missing |
| TRANS has 40292 (19.83%) missing values | Missing |
| TYPEMPL has 134202 (66.06%) missing values | Missing |
| TYPMENR has 3300 (1.62%) missing values | Missing |
| ANNINS is skewed | Skewed |
| CNAT is skewed | Skewed |
| CPAYSN is skewed | Skewed |
| GAD is skewed | Skewed |
| APE has a high cardinality: 369 distinct values | High Cardinality |
Dataset Insights
| MINE has constant value "1.0" | Constant |
|---|---|
| PROV has constant value "Sud" | Constant |
| APE has constant length 5 | Constant Length |
| CPAYSRA has constant length 5 | Constant Length |
| EMPL has constant length 3 | Constant Length |
| EXER has constant length 3 | Constant Length |
| GENRE has constant length 1 | Constant Length |
| ILN has constant length 1 | Constant Length |
| IRA has constant length 3 | Constant Length |
| MINE has constant length 3 | Constant Length |
Dataset Insights
| NAT has constant length 1 | Constant Length |
|---|---|
| PROV has constant length 3 | Constant Length |
| RECH has constant length 3 | Constant Length |
| SAL has constant length 3 | Constant Length |
| SCOL has constant length 3 | Constant Length |
| SECT10 has constant length 2 | Constant Length |
| SECT21 has constant length 1 | Constant Length |
| SECT5 has constant length 3 | Constant Length |
| STAT has constant length 3 | Constant Length |
| STATANT has constant length 3 | Constant Length |
Dataset Insights
| STM has constant length 1 | Constant Length |
|---|---|
| TACT has constant length 3 | Constant Length |
| TP has constant length 3 | Constant Length |
| TRAANT has constant length 3 | Constant Length |
| TRANS has constant length 3 | Constant Length |
| TYP has constant length 1 | Constant Length |
| TYPEMPL has constant length 3 | Constant Length |
| TYPMENR has constant length 3 | Constant Length |
| GAD has 28705 (14.13%) zeros | Zeros |
| GAQ has 13748 (6.77%) zeros | Zeros |
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Number of plots per page:
#df2 = df[['ID', 'DIPL', 'EMPL']]
#df2 = df2.fillna(0)
#df2.head()
#df2 = df2.pivot_table('DIPL', 'EMPL', 'ID', aggfunc="sum")
#f, ax = plt.subplots(figsize=(9, 6))
#sns.heatmap(df2, annot=True, linewidths=.5, ax=ax)
df_age = df[['AGER', 'GENRE']]
df_homme = df_age.loc[df_age['GENRE'] == 1].groupby('AGER').sum()
df_homme['AGER'] = df_homme.index
print(df_homme.shape)
print(df_homme.loc[df_homme['AGER'] == 40])
df_homme['GENRE'] = 0-df_homme['GENRE']
df_homme = df_homme.rename(columns={'GENRE': 'homme'})
df_femme = df_age.loc[df_age['GENRE'] == 2].groupby('AGER').sum()
df_femme = df_femme.rename(columns={'GENRE': 'femme'})
df_femme['AGER'] = df_femme.index
print(df_femme.shape)
df_femme.loc[df_femme['AGER'] == 40]
(101, 2)
GENRE AGER
AGER
40 1349 40
(105, 2)
| femme | AGER | |
|---|---|---|
| AGER | ||
| 40 | 3094 | 40 |
Pyramide des ages¶
sns.set(font_scale = 2)
df4 = pd.concat([df_homme, df_femme], axis=1).iloc[::-1]
figure = plt.figure(figsize=(50, 50))
bar_plot = sns.barplot(x='homme', y=df4.index, data=df4, order=df4.index, lw=0, orient='horizontal')
bar_plot = sns.barplot(x='femme', y=df4.index, data=df4, order=df4.index, lw=0, orient='horizontal')
bar_plot.set(ylabel="Age", xlabel="Nombre de personnes", title = "Pyramide des âges")
plt.plot([0,0], [0,105], linewidth=2)
#sns;barplot(data=df_age, x=)
[<matplotlib.lines.Line2D at 0x7f94bed01df0>]
Relation entre niveau de diplome, type d’emploi et catégorie socioprofessionnelle¶
df_metier = df[['DIPL', 'EMPL', 'CS8']].dropna()
sns.set(font_scale = 2)
fig, ax = plt.subplots(figsize=(50, 30))
df_counts = df_metier.groupby(['DIPL', 'EMPL']).size().reset_index()
df_counts.columns.values[df_counts.columns == 0] = 'count'
scale = 500*df_counts['count'].size
size = df_counts['count']/df_counts['count'].sum()*scale
#size = size.astype(float)
#sns.stripplot(x='DIPL', y='EMPL', hue='CS8', data=df_metier, ax=ax) #, size=size, sizes=(10,500)
dipl_id = [1, 2, 3, 4, 11, 12, 13, 14, 15, 16, 17, 18, 19]
dipl_lbl = ['Pas de scolarisation', 'Aucun diplôme mais scolarisation jusqu’en primaire', 'Aucun diplôme mais scolarisation jusqu’au collège',
'Aucun diplôme mais scolarisation au-delà du collège', 'CEP' , 'BEPC, brevet élémentaire, brevet des collèges, DNB' , 'CAP, BEP ou diplôme de niveau équivalent',
'Bac général ou technologique, brevet supérieur, capacité en droit, DAEU, ESEU',
'Bac professionnel, brevet professionnel de technicien ou d’enseignement, diplôme équivalent',
'BTS, DUT, Deug, Deust, diplôme de santé ou du social niveau bac + 2, diplôme équivalent',
'Licence, Licence pro, maîtrise, diplôme équivalent de niveau bac + 3 ou bac + 4',
'Master, DEA, diplôme grande école niveau bac + 5, doctorat de santé',
'Doctorat de recherche (hors santé)']
#plt.xticks(dipl_id, dipl_lbl, rotation=45, )
empl_lbl = ['Artisan, commerçant, industriel, travailleur indépendant', 'Stagiaire rémunéré, apprenti sous contrat',
'Salarié du secteur privé à durée déterminée', 'Salarié du secteur privé à durée indéterminée',
'Salarié du secteur public à durée déterminée', 'Salarié du secteur public à durée indéterminé']
from sklearn.preprocessing import OrdinalEncoder
import numpy as np
ord_enc = OrdinalEncoder()
enc_df = pd.DataFrame(ord_enc.fit_transform(df_metier), columns=list(df_metier.columns))
xnoise, ynoise = np.random.random(len(df_metier))/2, np.random.random(len(df_metier))/2
sns.scatterplot(enc_df["DIPL"]+xnoise, enc_df["EMPL"]+ynoise, alpha=0.5, hue=enc_df['CS8'], palette="hls")
plt.yticks(np.arange(0.25, len(empl_lbl)+0.25, 1), empl_lbl)
xrange = np.arange(0.25, len(dipl_lbl)+0.25, 1)
plt.xticks(xrange, dipl_lbl, rotation=90)
plt.legend(title='Categories socioprofessionnelles', loc='lower left', labels=['Agriculteurs exploitants', 'Artisans, commerçants et chefs d\'entreprise',
'Cadres et professions intellectuelles supérieures', 'Professions Intermédiaires',
'Employés', 'Ouvriers'])
/opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
warnings.warn(
<matplotlib.legend.Legend at 0x7f94deb0bf40>
